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import torch |
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import comfy.utils |
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def convert_lora_bfl_control(sd): |
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sd_out = {} |
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for k in sd: |
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k_to = "diffusion_model.{}".format(k.replace(".lora_B.bias", ".diff_b").replace("_norm.scale", "_norm.scale.set_weight")) |
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sd_out[k_to] = sd[k] |
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sd_out["diffusion_model.img_in.reshape_weight"] = torch.tensor([sd["img_in.lora_B.weight"].shape[0], sd["img_in.lora_A.weight"].shape[1]]) |
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return sd_out |
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def convert_lora_wan_fun(sd): |
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return comfy.utils.state_dict_prefix_replace(sd, {"lora_unet__": "lora_unet_"}) |
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def convert_uso_lora(sd): |
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sd_out = {} |
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for k in sd: |
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tensor = sd[k] |
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k_to = "diffusion_model.{}".format(k.replace(".down.weight", ".lora_down.weight") |
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.replace(".up.weight", ".lora_up.weight") |
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.replace(".qkv_lora2.", ".txt_attn.qkv.") |
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.replace(".qkv_lora1.", ".img_attn.qkv.") |
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.replace(".proj_lora1.", ".img_attn.proj.") |
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.replace(".proj_lora2.", ".txt_attn.proj.") |
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.replace(".qkv_lora.", ".linear1_qkv.") |
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.replace(".proj_lora.", ".linear2.") |
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.replace(".processor.", ".") |
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) |
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sd_out[k_to] = tensor |
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return sd_out |
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def convert_lora(sd): |
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if "img_in.lora_A.weight" in sd and "single_blocks.0.norm.key_norm.scale" in sd: |
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return convert_lora_bfl_control(sd) |
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if "lora_unet__blocks_0_cross_attn_k.lora_down.weight" in sd: |
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return convert_lora_wan_fun(sd) |
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if "single_blocks.37.processor.qkv_lora.up.weight" in sd and "double_blocks.18.processor.qkv_lora2.up.weight" in sd: |
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return convert_uso_lora(sd) |
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return sd |
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